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Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models

Overview of attention for article published in Biomechanics & Modeling in Mechanobiology, September 2017
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (54th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

twitter
2 tweeters

Citations

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2 Dimensions

Readers on

mendeley
20 Mendeley
Title
Multifidelity-CMA: a multifidelity approach for efficient personalisation of 3D cardiac electromechanical models
Published in
Biomechanics & Modeling in Mechanobiology, September 2017
DOI 10.1007/s10237-017-0960-0
Pubmed ID
Authors

Roch Molléro, Xavier Pennec, Hervé Delingette, Alan Garny, Nicholas Ayache, Maxime Sermesant

Abstract

Personalised computational models of the heart are of increasing interest for clinical applications due to their discriminative and predictive abilities. However, the simulation of a single heartbeat with a 3D cardiac electromechanical model can be long and computationally expensive, which makes some practical applications, such as the estimation of model parameters from clinical data (the personalisation), very slow. Here we introduce an original multifidelity approach between a 3D cardiac model and a simplified "0D" version of this model, which enables to get reliable (and extremely fast) approximations of the global behaviour of the 3D model using 0D simulations. We then use this multifidelity approximation to speed-up an efficient parameter estimation algorithm, leading to a fast and computationally efficient personalisation method of the 3D model. In particular, we show results on a cohort of 121 different heart geometries and measurements. Finally, an exploitable code of the 0D model with scripts to perform parameter estimation will be released to the community.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 20 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 20 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 30%
Professor 3 15%
Student > Ph. D. Student 3 15%
Student > Postgraduate 2 10%
Student > Bachelor 2 10%
Other 4 20%
Readers by discipline Count As %
Computer Science 6 30%
Unspecified 5 25%
Engineering 5 25%
Agricultural and Biological Sciences 1 5%
Mathematics 1 5%
Other 2 10%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 29 September 2017.
All research outputs
#6,519,650
of 11,843,218 outputs
Outputs from Biomechanics & Modeling in Mechanobiology
#91
of 204 outputs
Outputs of similar age
#120,018
of 270,682 outputs
Outputs of similar age from Biomechanics & Modeling in Mechanobiology
#4
of 9 outputs
Altmetric has tracked 11,843,218 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 204 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.4. This one has gotten more attention than average, scoring higher than 52% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 270,682 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.